Tagging content with metadata pre-filtered by context

ABSTRACT

Generate tags for content from metadata pre-filtered based on context. A plurality of data items is accessed. Each of the data items has metadata. A context for a user is determined (e.g., at a moment of content capture). One or more of the data items are selected based on the determined context. Upon receipt of content, the received content is compared with the selected data items to identify matches. Metadata is selected from the metadata associated with the matching data items. The selected metadata is associated with the captured content.

BACKGROUND

The availability of content recording functionality on computing deviceshas furthered the popularity of social networking applications. Forexample, with the cameras and voice recorders included in many mobiletelephones, users are capturing and providing more content than everbefore. With the increased amount of captured content, some users haveattempted to manually annotate or tag each content item with a captionor identifier. However, this manual process is time-consuming andlaborious.

Some existing systems have attempted to automate content annotation bysuggesting tags based on the location of the mobile telephone at thetime of capture. The suggestions take the form of physical and culturalfeatures obtained from a Geographic Names Information System (GNIS)database. Other existing systems attempt to identify an event associatedwith the content by comparing the date and time of content capture tocalendar entries of the user. Each of these two example systems,however, is directed to a specific database of annotations, and thus islimited in the type of annotations provided.

SUMMARY

Embodiments of the disclosure enable context-enriched automatic tagging.Each of a plurality of data items associated with a user has metadataassociated therewith. A context for the user is determined. One or moreof the plurality of data items are selected based on the determinedcontext. Received content is compared with the selected data items toenable identification of one or more of the selected data items.Metadata is selected from the metadata associated with the identifieddata items. The selected metadata is associated with the receivedcontent.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary block diagram illustrating a computing devicetagging newly-acquired content with metadata from previously-acquiredcontent.

FIG. 2 is an exemplary block diagram illustrating a mobile computingdevice matching received content with stored data items based on usercontext.

FIG. 3 is an exemplary flow chart illustrating a method forcontext-enriched automatic tagging of acquired content.

FIG. 4 is an exemplary block diagram illustrating an intelligent taggingengine generating metadata tags for captured content.

FIG. 5 is an exemplary block diagram illustrating the creation ofmetadata tags for content based in part on user actions.

FIG. 6 is an exemplary block diagram illustrating the recognition of aface within an image based on data items associated with a user.

Corresponding reference characters indicate corresponding partsthroughout the drawings.

DETAILED DESCRIPTION

Referring to the figures, embodiments of the disclosure enable, atleast, the automated recognition and tagging of content using metadata108 pre-filtered by context information. The pre-filtered metadatarepresents possibly relevant metadata identified using the contextinformation. The pre-filtered metadata may be referred to as a“fingerprint.” The fingerprint reduces the amount of metadata to search,reduces the amount of user input when creating tags, enablespersonalized tagging,

Referring again to FIG. 1, an exemplary block diagram illustrates acomputing device 102 tagging newly-acquired content 104 with metadata108 from previously-acquired content 106. The elements illustrated inFIG. 1 operate to enable context-enriched automatic tagging. In someembodiments, the computing device 102 represents a plurality ofcomputing devices programmed to implement the functionality describedherein. The computing device 102 determines the context for a userand/or computing device 102. Determining the context includes, forexample, determining a location of the computing device 102 or user, ordetermining one or more of the following user-specific data: userbehavior, user activity, user purchase history, search query history,profile data, user files, and an application program being executed tocapture the content.

Alternatively or in addition, the context may be explicitly defined bythe user (e.g., the user selects a pre-defined profile or otherwiseprovides the context). The context is determined at the approximate timeof acquiring the content (e.g., shortly before or after the content isacquired). Alternatively or in addition, the context may be determinedsome time prior to acquiring the content (e.g., the user and computingdevice 102 have been in the same location). The context may bedetermined or updated each time content is acquired, or once for aplurality of instances of content acquisition.

The computing device 102 identifies metadata 108 that is possiblyrelevant based on the determined context. The metadata 108 is identifiedfrom previously-acquired content. Identifying the possibly relevantmetadata may be referred to as inferring a fingerprint, pre-filteringthe metadata 108, narrowing the metadata 108 to be searched, or thelike. In some embodiments, the possibly relevant metadata is identifiedbefore the content is acquired or otherwise accessed. In otherembodiments in which the content has been acquired, the possiblyrelevant metadata is identified independent of the content or otherwisewithout reliance on the content. Further, the identified, possiblyrelevant metadata is applicable at least until the determined contextchanges.

The possibly relevant metadata may include metadata associated with thedetermined context. In some embodiments, the possibly relevant metadataincludes metadata matching a location of the computing device 102,application programs being executed on or off the computing device 102(e.g., application domains), a web browsing history of the user, imagesfrom a contacts list, meeting places in calendar entries, activities ina task list, products from products catalogs, or other metadatadetermined by the context where the content is captured.

The content may be obtained, retrieved, received, accessed, pushed,pulled, or the like. For example, the computing device 102 may activelyseek the content or may passively receive the content, in variousembodiments. The content includes, for example, audio, text, video, andimages.

The previously-acquired content 106 includes pre-existing structureddata and unstructured data, including data acquired by the user and/ordata acquired by other users. The previously-acquired content 106 may bestored local to the computing device 102 (e.g., in memory internal tothe computing device 102) or remote from the computing device 102 (e.g.,in cloud storage). The previously-acquired content 106 may also havebeen acquired by other users, or merely provided by content providers.For example, the previously-acquired content 106 may include data storedon social network, or other private or public repositories. In someembodiments, the computing device 102 accesses the previously-acquiredcontent 106 stored local to the computing device 102 and, depending onresource constraints such as bandwidth and battery level, also accessesthe previously-acquired content 106 stored remote from the computingdevice 102.

Metadata 108 describing the previously-acquired content 106 isassociated with the previously-acquired content 106. The metadata 108may be stored with the previously-acquired content 106, or separate fromthe previously-acquired content 106.

In some embodiments, the computing device 102 analyzes thenewly-acquired content 104 using the identified, possibly relevantmetadata to identify matches with the previously-acquired content 106.For example, an image (or a portion thereof) from thepreviously-acquired content 106 may be found in the newly-acquiredcontent 104 using image recognition algorithms. The computing device 102retrieves the metadata 108 associated with the matching content from thepreviously-acquired content 106. The retrieved metadata is associatedwith the newly-acquired content 104. In some embodiments, thenewly-acquired content 104 and retrieved metadata are stored as part ofthe previously-acquired content 106 and metadata 108, respectively.

In other embodiments, the operations illustrated in FIG. 1 are performedby another computing device (e.g., executing a web service to providethe metadata for the newly-acquired content 104).

Referring next to FIG. 2, an exemplary block diagram illustrates amobile computing device 202 matching received content 206 with storeddata items 212 based on user context. The mobile computing device 202includes at least a memory area 210 and a processor 208. The memory area210, or other computer-readable media, stores the plurality of dataitems 212 such as data item #1 through data item #N. In someembodiments, the data items 212 include, but are not limited to, one ormore of the following: calendar entries, task entries, contacts, imagefiles, video files, audio files, objects within the image files, objectswithin the video files, blog entries, and microblog entries.Alternatively or in addition, the data items 212 represent metadatasources. The data items 212 may be stored locally as in the example ofFIG. 2, or stored remotely such as by a web service (e.g., a socialnetworking web site). In some embodiments, the data items 212 areassociated with a user 204 of the mobile computing device 202. In otherembodiments, the data items 212 may be associated with one or more of aplurality of users. Each of the plurality of data items 212 has metadataitems 214 associated therewith, such as metadata item #1 throughmetadata item #N.

The mobile computing device 202 further includes one or more interfacessuch as input interface 224. The interfaces include, for example, anetwork interface, a camera, an audio recording device, or any otherinterface for receiving content 206.

The memory area 210, or one or more computer-readable media, furtherstores computer-executable components for implementing aspects of thedisclosure. Exemplary components include a context component 216, afingerprint component 218, a recognition component 220, and a tagcomponent 222. These components are described below with reference toFIG. 3.

In general, the memory area 210 is associated with the mobile computingdevice 202. For example, in FIG. 2, the memory area 210 is within themobile computing device 202. However, the memory area 210 includes anymemory area internal to, external to, or accessible by mobile computingdevice 202. Further, the memory area 210 or any of the data storedthereon may be associated with any server or other computer, local orremote from the mobile computing device 202 (e.g., accessible via anetwork).

The processor 208 includes any quantity of processing units, and isprogrammed to execute computer-executable instructions for implementingaspects of the disclosure. The instructions may be performed by theprocessor 208 or by multiple processors executing within the mobilecomputing device 202, or performed by a processor external to the mobilecomputing device 202 (e.g., by a cloud service). In some embodiments,the processor 208 is programmed to execute instructions such as thoseillustrated in the figures (e.g., FIG. 3).

Referring next to FIG. 3, an exemplary flow chart illustrates a methodfor context-enriched automatic tagging of acquired content. In someembodiments, the operations illustrated in FIG. 3 are performed by thecomputing device 102 or the mobile computing device 202. In otherembodiments, one or more of the operations illustrated in FIG. 3 areperformed by another computing device (e.g., as a web service). At 302,the context is determined. For example, one or more keywords may beidentified (e.g., Phoenix, Meeting with Bill, Conference Room A,Document Editor, Web Browser). One or more of the data items 212 areselected at 304 based on the determined context. The selected data itemsrepresent or define a fingerprint as described herein. When content isreceived (or accessed, retrieved, etc.) at 306, the received content iscompared at 308 to the selected data items. For example, the receivedcontent may be searched for keywords determined from the context. In anexample in which the selected data items include images of contacts froman address book and the received content is an image, the received imageis compared to images of contacts to find matches. For example, facialrecognition algorithms attempt to find the photos from the address bookin the received image (e.g., the received image may include severalfaces).

Further, the received image may be compared to images from an imagelibrary of the user 204. For example, the received image may be searchedfor landmarks, people, text, and more from images in the image library.In such an example, aspects of the disclosure attempt to identify atleast a portion of the images from the image library in the receivedimage. If there are matching data items, the metadata associated withthe matching data items is obtained at 310. The obtained metadata isassociated with the received content at 312. For example, the receivedcontent is stored as one of the data items 212, and the obtainedmetadata is stored therewith.

In some embodiments, prior to associating the metadata with the receivedcontent, the metadata is presented to the user 204 for confirmation orediting. For example, the user 204 may accept the suggested metadata,manually edit the suggested metadata, or reject the suggestions.

In some embodiments, one or more computer-executable components, such asthe components illustrated in FIG. 2, execute on the computing device102 to perform the operations illustrated in FIG. 3. The contextcomponent 216, when executed by the processor 208, causes the processor208 to determine the context for the user 204. The fingerprint component218, when executed by the processor 208, causes the processor 208 tosearch for one or more of the data items 212 based on the determinedcontext. For example, the fingerprint component 218 searches thecomputing device 102 of the user 204 and/or searches storage areasremote from the computing device 102 (e.g., social networking web sites,blogs, microblogs, etc.). The recognition component 220, when executedby the processor 208, causes the processor 208 to compare capturedcontent with the data items 212 from the fingerprint component 218 andto identify one or more of the selected data items based on thecomparison. The tag component 222, when executed by the processor 208,causes the processor 208 to define metadata for the accessed contentbased on the metadata associated with the data items identified by therecognition component 220. The tag component 222 further associates thedefined metadata with the captured content.

Referring next to FIG. 4, an exemplary block diagram illustrates anintelligent tagging engine 402 generating metadata tags 410 for capturedcontent. In some embodiments, the tagging engine 402 executes on themobile computing device 202 (e.g., see FIG. 2). In other embodiments, atleast a portion of the functionality of the tagging engine 402 isperformed by an entity (e.g., processor, web service, server,application program, computing device, etc.) remote from the computingdevice 102 of the user.

The tagging engine 402 determines context 404 for the computing device102 of the user at 401. The context 404 includes, for example, thecurrent time, a location of the computing device 102 or the user, one ormore task entries, one or more calendar entries, a browsing context, atransaction history for the user, user interaction with the computingdevice 102, or a mode of movement of the computing device 102. Othercontextual information is contemplated by aspects of the disclosure.Possibly relevant metadata is identified (e.g., one or more fingerprints406 are defined). The fingerprints 406 may be inferred from one or moreapplication programs 408 executing on or off the computing device 102 at403 (e.g., the application domain). The relevant fingerprints 406 mayinclude, for example, images in application programs (e.g., messagingapplications) that use contacts. The user may explicitly define thefingerprints 406 at 405 (e.g., the user selects “faces” in images). Inthe example of FIG. 4, the tagging engine 402 uses the determinedcontext 404 to define or refine, at 407, the fingerprints 406. Forexample, location and proximity information may be used to furthernarrow the potential images to match. The fingerprints 406 may include aplurality of defined fingerprints 406 cached and available to the userwho is engaged in content capture. In some embodiments, the userexplicitly selects one or more of the cached fingerprints 406 before,during, or after content capture.

The application programs 408 capture the content at 409 (e.g., thenewly-acquired content 104). The tagging engine 402 uses the definedfingerprints 406 to match objects in the newly-acquired content 104 at411. The tagging engine 402 may also retrieve on-device metadata (e.g.,metadata stored on the computing device 102) with matching fingerprintsat 413. The tagging engine 402 may also retrieve or search existingmetadata 412 at 415 with matching fingerprints. The tagging engine 402creates tags 410 at 417 based on the metadata relevant to the capturedcontent.

Referring next to FIG. 5, an exemplary block diagram illustrates thecreation of metadata tags 410 for content based in part on user actions.The tagging engine 402 extracts fingerprints at 501 based on contextand/or existing metadata 412. Newly-acquired content 104 is processed bythe tagging engine 402 at 502. The tagging engine 402 generates tags 410for the newly-acquired content 104 at 503. The user rates the generatedtags 410 at 504. The tagging engine 402 processes the user ratings at505. The tagging engine 402 adjusts the existing metadata 412 at 506based on the newly-acquired content 104 and the user ratings.

Referring next to FIG. 6, an exemplary block diagram illustrates therecognition of a face within an image based on data items 212 associatedwith the user 204. The user 204 executes an image capture applicationprogram on a mobile telephone 602. Aspects of the disclosure, executingon the mobile telephone 602, define a fingerprint based on the context.The context at least includes, in this example, that the image captureapplication program is executing, a location of the mobile telephone 602(e.g., from a location provider from a calendar entry), a current time,and identification of the user 204. A fingerprint is defined based onthe determined context. For example, the defined fingerprint indicatesthat possibly relevant metadata includes images from an address book 604of the identified user 204, and one or more events corresponding to thecurrent time (e.g., the moment of image capture) from calendar entriesfor the user 204 (e.g., including event descriptions, location names,and attendees for the events).

The user 204 takes a picture with the mobile telephone 602. The imagecapture application program captures the image and compares the capturedimage to the fingerprint (e.g., compares the captured image to thepossibly relevant metadata). In this example, the captured image iscompared to the images from the address book 604 of the identified user204, and the events corresponding from the calendar entries for the user204 (e.g., the event descriptions, location names, and attendees aresearched for within the captured image). In this example, there is animage of “Sally Miles” in the address book 604 of the user 204.Alternatively or in addition, the image of Sally Miles (or other images)may be taken from a web page of the user 204 on a social network website 606. Aspects of the disclosure compare the image of Sally Milesfrom the address book 604 to the captured image (e.g., using facialrecognition analysis). In this example, a match is found between theimage of Sally Miles from the address book 604 and one of the faces inthe captured image. The tag “Sally Miles” is then created for thecaptured image.

Similarly, if the fingerprint included the event attendee “Jennifer”,the event location “Joey's at Bellevue”, and the event description“Dinner,” aspects of the disclosure suggest these additional tags forassociation with the captured image.

Further Examples

Various implementations of the disclosure are contemplated. For example,embodiments of the disclosure recognize objects in a captured imageusing pictures or logos from a product catalog. The particular productcatalog is selected based on, for example, recent purchasing or browsingby the user. In this example, the user captures the image while shoppingat a mall.

In another example, an address book application program launches acamera application to capture an image of a contact. The tagging engine402 infers, based on the execution of the address book application, thata relevant metadata source is the contacts database.

In still another example, the user browses the Internet for a newelectronics product. The user sorts the available products based onprice. In this example, the browsing behavior of sorting by price is oneof the data items 212, while price is the metadata associated with thedata item. Subsequently, aspects of the disclosure detect that the userenters a store (e.g., the context is determined). The stored browsingbehavior and one or more electronic products catalogs are identified aspossibly relevant based on the determined context (e.g., being in thestore). Upon capture of an image of an electronics product in the store(e.g., the user takes a picture of a product), the image is compared tothe electronic products catalogs. If there is a match, metadataassociated with the matching product from the products catalogs isassociated with the captured image. In this example, because of theprevious browsing history, the price of the product is included asmetadata for the captured image.

Exemplary Operating Environment

By way of example and not limitation, computer readable media comprisecomputer storage media and communication media. Computer storage mediastore information such as computer readable instructions, datastructures, program modules or other data. Communication media typicallyembody computer readable instructions, data structures, program modules,or other data in a modulated data signal such as a carrier wave or othertransport mechanism and include any information delivery media.Combinations of any of the above are also included within the scope ofcomputer readable media.

Although described in connection with an exemplary computing systemenvironment, embodiments of the invention are operational with numerousother general purpose or special purpose computing system environmentsor configurations. Examples of well known computing systems,environments, and/or configurations that may be suitable for use withaspects of the invention include, but are not limited to, mobilecomputing devices, personal computers, server computers, hand-held orlaptop devices, multiprocessor systems, gaming consoles,microprocessor-based systems, set top boxes, programmable consumerelectronics, mobile telephones, network PCs, minicomputers, mainframecomputers, distributed computing environments that include any of theabove systems or devices, and the like.

Embodiments of the invention may be described in the general context ofcomputer-executable instructions, such as program modules, executed byone or more computers or other devices. The computer-executableinstructions may be organized into one or more computer-executablecomponents or modules. Generally, program modules include, but are notlimited to, routines, programs, objects, components, and data structuresthat perform particular tasks 310 or implement particular abstract datatypes. Aspects of the invention may be implemented with any number andorganization of such components or modules. For example, aspects of theinvention are not limited to the specific computer-executableinstructions or the specific components or modules illustrated in thefigures and described herein. Other embodiments of the invention mayinclude different computer-executable instructions or components havingmore or less functionality than illustrated and described herein.

Aspects of the invention transform a general-purpose computer into aspecial-purpose computing device when configured to execute theinstructions described herein.

The embodiments illustrated and described herein as well as embodimentsnot specifically described herein but within the scope of aspects of theinvention constitute exemplary means for context-enriched automatictagging of the captured content based on the determined context and theselected data items 212, and exemplary means for inferring metadata forthe captured content at the moment of capture.

The order of execution or performance of the operations in embodimentsof the invention illustrated and described herein is not essential,unless otherwise specified. That is, the operations may be performed inany order, unless otherwise specified, and embodiments of the inventionmay include additional or fewer operations than those disclosed herein.For example, it is contemplated that executing or performing aparticular operation before, contemporaneously with, or after anotheroperation is within the scope of aspects of the invention.

When introducing elements of aspects of the invention or the embodimentsthereof, the articles “a,” “an,” “the,” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

Having described aspects of the invention in detail, it will be apparentthat modifications and variations are possible without departing fromthe scope of aspects of the invention as defined in the appended claims.As various changes could be made in the above constructions, products,and methods without departing from the scope of aspects of theinvention, it is intended that all matter contained in the abovedescription and shown in the accompanying drawings shall be interpretedas illustrative and not in a limiting sense.

1. A system for context-enriched automatic tagging, said systemcomprising: a memory area for storing a plurality of data items of auser of a mobile computing device, each of the plurality of data itemshaving metadata associated therewith, said memory area being associatedwith the mobile computing device; and a processor programmed to:determine a context for the user when capturing content, said determinedcontext including a location of the mobile computing device anduser-specific data including one or more messages; pre-filter theplurality of data items by the determined context, the pre-filtered dataitems representing data items having metadata identified using thedetermined context, the pre-filtered data items indicating that themetadata includes one or more images from an address book of the userand one or more events corresponding to a current time from one or morecalendar entries for the user; select one or more of the pre-filtereddata items stored in the memory area based on the determined context;capture the content; compare the captured content with one or more ofthe selected data items; identify one or more of the selected data itemsbased on the comparison; define metadata for the captured content basedon the metadata associated with one or more of the identified dataitems; and store the captured content with the defined metadata in thememory area as a data item in the plurality of data items.
 2. The systemof claim 1, wherein the plurality of data items comprises one or more ofthe following: calendar entries, task entries, contacts, image files,video files, audio files, objects within the image files, and objectswithin the video files.
 3. The system of claim 1, wherein the processoris programmed to determine the context based on the location associatedwith the mobile computing device.
 4. The system of claim 1, wherein theprocessor is programmed to determine the context based on one or more ofthe following: user behavior, user activity, user purchase history,search query history, and an application program being executed tocapture the content.
 5. The system of claim 1, wherein the processor isfurther programmed to transmit the plurality of data items to a datastorage area accessible by the mobile computing device via a network. 6.The system of claim 1, wherein the processor is further programmed topresent the defined metadata for the captured content to the user forconfirmation before storing the captured content with the definedmetadata in the memory area.
 7. The system of claim 1, furthercomprising means for context-enriched automatic tagging of the capturedcontent based on the determined context and one or more of the selecteddata items.
 8. The system of claim 1, further comprising means forinferring metadata for the captured content at the moment of capture. 9.A method comprising: accessing a plurality of data items on at least acomputing device associated with a user, each of the plurality of dataitems having metadata associated therewith; determining a context forthe user, said determined context including a location of the computingdevice and user-specific data including one or more messages;pre-filtering the plurality of data items by the determined context, thepre-filtered data items indicating that the metadata includes one ormore images from an address book of the user and one or more eventscorresponding to a current time from one or more calendar entries forthe user; selecting one or more of the pre-filtered data items based onthe determined context; receiving content; comparing the receivedcontent with one or more of the selected data items; identifying one ormore of the selected data items based on said comparing; selectingmetadata from the metadata associated with one or more of the identifieddata items; and associating the selected metadata with the receivedcontent.
 10. The method of claim 9, further comprising storing thereceived content with the associated metadata as a data item in theplurality of data items, the metadata associated with one or more of theidentified data items being applicable until the determined context forthe user changes.
 11. The method of claim 9, wherein determining thecontext for the user comprises identifying keywords, and whereincomparing the received content comprises searching the received contentfor the identified keywords.
 12. The method of claim 9, whereinaccessing the plurality of data items comprises accessing the addressbook and a social networking website associated with the user.
 13. Themethod of claim 9, further comprising: presenting the selected metadatato the user, wherein the user edits the presented metadata; andreceiving edited metadata from the user, wherein associating theselected metadata includes associating the edited metadata with thereceived content.
 14. The method of claim 9, wherein selecting the oneor more of the plurality of data items comprises selecting a firstimage, wherein receiving content comprises receiving a second image, andwherein comparing the received content with the selected data itemcomprises identifying at least a portion of the first image within thesecond image.
 15. The method of claim 9, wherein determining a contextfor the user comprises determining the location of the computing device,and wherein selecting one or more of the plurality of data itemscomprises selecting one or more data items having metadata associatedwith the location.
 16. One or more computer storage media storingcomputer-executable components, said components comprising: a contextcomponent that when executed by at least one processor causes the atleast one processor to determine a context for a user; a fingerprintcomponent that when executed by at least one processor causes the atleast one processor to search on at least a computing device associatedwith the user for one or more data items based on the determinedcontext, each of the data items having metadata associated therewith,the searching comprising pre-filtering the one or more data items basedon the determined context, said determined context including a locationof the computing device associated with the user and user-specific dataincluding one or more messages, the pre-filtered data items indicatingthat the metadata includes one or more images from an address book ofthe user and one or more events corresponding to a current time from oneor more calendar entries for the user; a recognition component that whenexecuted by at least one processor causes the at least one processor tocompare captured content with one or more of the pre-filtered data itemsfrom the fingerprint component and to identify one or more of thepre-filtered data items based on the comparison; and a tag componentthat when executed by at least one processor causes the at least oneprocessor to define metadata for the captured content based on themetadata associated with the data items identified by the recognitioncomponent and to associate the defined metadata with the capturedcontent.
 17. The computer storage media of claim 16, wherein thefingerprint component searches for one or more of the data items onsocial networking websites.
 18. The computer storage media of claim 16,wherein the tag component prompts the user to accept the definedmetadata before associating the defined metadata with the capturedcontent.
 19. The computer storage media of claim 16, wherein thecaptured content comprises an image of one or more people, and whereinthe recognition component compares the captured content with one or moreof the data items from the fingerprint component by performing a facialrecognition analysis.